"""
Q, R = randomized_range_finder(A, l=l)
return direct_svd(A, Q)
def algo_3(A, l=8, method='srft', verbose=False):
"""
Computes the third algorithm
"""
if verbose:
print "Computing Fast Randomized Range Finder"
Q, _ = fast_randomized_range_finder(A, l=l, method=method)
if verbose:
print "Compute direct SVD"
# return row_extraction_svd(A, Q)
return direct_svd(A, Q)
U1, S1, V1 = mem.cache(algo_1)(gaussian)
U2, S2, V2 = mem.cache(algo_2)(gaussian, l=100)
U3, S3, V3 = mem.cache(algo_3)(gaussian, l=100)
draw_plots([S_ground_truth[:100], S1[:100], S2[:100], S3[:100]],
legend=('Ground truth', 'Algo1', 'Algo2', 'Algo3'),
logscale=False)
draw_plots([error(S_ground_truth[:100], S1[:100]),
error(S_ground_truth[:100], S2[:100]),
error(S_ground_truth[:100], S3[:100])],
legend=('Error 1', 'Error 2', 'Error 3'))
pca = PCA(n_components=100)
pca.fit(data)
p0 = pca.explained_variance_
rpca = RandomizedPCA(n_components=100, iterated_power=1)
rpca.fit(data)
p1 = rpca.explained_variance_
rpca = RandomizedPCA(n_components=100, iterated_power=2)
rpca.fit(data)
p2 = rpca.explained_variance_
rpca = RandomizedPCA(n_components=100, iterated_power=3)
rpca.fit(data)
p3 = rpca.explained_variance_
draw_plots([gt[:100], e0[:100], e1[:100], e2[:100], e3[:100]],
legend=('grountruth', 'q=0', 'q=1', 'q=2', 'q=3'))
draw_plots([error(gt[:100], e0[:100]),
error(gt[:100], e1[:100]),
error(gt[:100], e2[:100]),
error(gt[:100], e3[:100])],
legend=('Error 1', 'Error 2', 'Error 3', 'Error 4'))
draw_plots([p0[:100], p1[:100], p2[:100], p3[:100]],
legend=('grountruth', 'q=0', 'q=1', 'q=2', 'q=3'))
draw_plots([error(p0[:100], p1[:100]),
error(p0[:100], p2[:100]),
error(p0[:100], p3[:100])],
legend=('Error 1', 'Error 2', 'Error 3'))
from math import pi
from draw import draw_plots
from signal import error_points_by_M, \
harmonic_signal, \
mean_from_power, \
mean_from_variance, \
fourier_amplitude, \
EXPECTED_MEAN, \
EXPECTED_AMPLITUDE
# Variant 1
PHASES = [0.0, pi / 32.0]
K_FROM_N = lambda N: 3 * N // 4
N = 64
LABELS = [
'Amplitude error',
'Mean from power error',
'Mean from variance error'
]
PLOT_FUNCTIONS = [
lambda phase: error_points_by_M(N, K_FROM_N(N), harmonic_signal, phase, EXPECTED_AMPLITUDE, fourier_amplitude),
lambda phase: error_points_by_M(N, K_FROM_N(N), harmonic_signal, phase, EXPECTED_MEAN, mean_from_power),
lambda phase: error_points_by_M(N, K_FROM_N(N), harmonic_signal, phase, EXPECTED_MEAN, mean_from_variance)
]
if __name__ == '__main__':
draw_plots(PHASES, PLOT_FUNCTIONS, LABELS)
B1 = 100
B2 = 2
if __name__ == '__main__':
random_signal_values = [test_signal(i, N, B1, B2) for i in range(N)]
moving_averaged_values = moving_averaged_antialiasing(
random_signal_values, AVERAGED_WINDOW)
parabola_values = fourth_degree_parabola_antialiasing(random_signal_values)
moving_median_values = moving_median_antialiasing(random_signal_values,
MEDIAN_WINDOW,
REMOVING_ELEMENTS)
signals = [
PlotDescription(values=random_signal_values,
label='Original',
visibility=True,
spectrum=amplitude_spectrum(random_signal_values)),
PlotDescription(values=moving_averaged_values,
label='Moving averaged',
visibility=False,
spectrum=amplitude_spectrum(moving_averaged_values)),
PlotDescription(values=parabola_values,
label='Fourth degree parabola',
visibility=False,
spectrum=amplitude_spectrum(parabola_values)),
PlotDescription(values=moving_median_values,
label='Moving median',
visibility=False,
spectrum=amplitude_spectrum(moving_median_values))
]
draw_plots(signals)